where \(\mathcal {L}_{wsl}\)  and \(\mathcal {L}_{g}\)  are borrowed from [1]} and \(\mathcal {L}_{ad}\)  is the adversarial loss discussed above. \(_{wsl}\)  is the main weakly supervised loss [1]} for event classification while \(_g\)  is the modality specific classification loss that is applied after label smoothing. These two components are also elaborated in the extended version of our paper. Rishabh to insert arxiv link here or earlier on in the paper
